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Visualizing feature importances

Your random forest classifier from earlier exercises has been fit to the telco data and is available to you as clf. Let's visualize the feature importances and get a sense for what the drivers of churn are, using matplotlib's barh to create a horizontal bar plot of feature importances.

This exercise is part of the course

Marketing Analytics: Predicting Customer Churn in Python

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Exercise instructions

  • Calculate the feature importances of clf.
  • Use plt.barh() to create a horizontal bar plot of importances.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Calculate feature importances
importances = ____.____

# Create plot
____.____(range(X.shape[1]), ____)
plt.show()
Edit and Run Code